Type 1 diabetes is a chronic, life-threatening disease that is caused by failure of the pancreas to deliver the hormone insulin, which is otherwise made and secreted by the beta cells of the pancreatic islets of Langerhans. With the resulting absence of endogenous insulin, people with type 1 diabetes cannot regulate their blood glucose to euglycemic range without exogenous insulin administration. Therefore, it is necessary for people with type 1 diabetes to monitor their blood glucose and administer exogenous insulin several times a day in a relentless effort to maintain their blood glucose near euglycemic range.
The existing blood glucose management devices assist a diabetic patient in managing their blood glucose levels during everyday routine. Some of these devices are insulin pumps that provide continuous delivery of insulin throughout the day. Others are, for example: glucose monitoring devices which measure blood glucose levels along a certain time line i.e. to obtain blood glucose reading; and Artificial Pancreas (AP) systems which automatically modulate insulin delivery (optionally other hormones) according to measured glucose levels.
Insulin pump allows the physician to preset the pump settings to many different basal rates to allow for variation in the patient's lifestyle. In addition, the physician can predetermine the insulin bolus delivery (large dose of insulin) to cover the excess demands of carbohydrate ingestion or to correct high blood glucose levels. These pump settings include: bloods glucose target levels, insulin basal rate; carbohydrate ratio (CR) or factor; correction factor (CF) and constant insulin activity function.
Normally, the physician receives from the patient personalized information which includes the glucose past trace (measured by glucometer in discrete points or using continuous glucose sensor), the insulin that was previously delivered (the detailed log of how many insulin was delivered—in either basal or bolus—over time), and the detailed log of the amount and time of all meals and physical activity of the diabetic patients. The physician thus needs to conduct a retrospective analysis (i.e., look at the log data during the clinical visit) and determine the insulin pump settings based on this information.
Various techniques have been developed aimed at facilitating the operation of the insulin delivery pump device. Such techniques are disclosed for example in the following patent publications:
US Publication No. 2008/0228056 discloses an apparatus comprising a user interface configured to generate an electrical signal to start a basal insulin rate test when prompted by a user, an input configured to receive sampled blood glucose data of a patient that is obtained during a specified time duration, including a time duration during delivery of insulin according to a specified basal insulin rate pattern, and a controller communicatively coupled to the input and the user interface. The controller includes an insulin calculation module.
U.S. Pat. No. 7,751,907 discloses an apparatus comprising a controller; the controller includes an input/output (I/O) module and a rule module; the I/O module is configured to present a question for a patient when communicatively coupled to a user interface and receive patient information in response to the question via the user interface; the rule module is configured to apply a rule to the patient information and generate a suggested insulin pump setting from application of the rule.
US Publication No. 2008/0206799 discloses an apparatus comprising a user interface configured to generate an electrical signal to begin a carbohydrate ratio test when prompted by a user, an input configured to receive sampled blood glucose data of a patient that is obtained during specified time duration, and a controller in electrical communication with the input and the user interface. The controller includes a carbohydrate ratio suggestion module.
U.S. Pat. No. 7,734,323 discloses an apparatus comprising a user interface configured to generate an electrical signal to begin determination of an effective correction factor when prompted by a user, an input configured to receive sampled blood glucose data of a patient that is obtained during a specified time duration, and a controller in electrical communication with the input and the user interface. The controller includes a correction factor suggestion module.
On the other side, the artificial pancreas systems are usually based either on traditional linear control theory or rely on mathematical models of glucose-insulin dynamics. The most common techniques are based on proportional-integral-derivative control (PID) [1] and model predictive control (MPC) [2-5]. However, the nonlinearity, complexity and uncertainty of the biological system along with the inherited delay and deviation of the measuring devices, makes difficult to define a model and correctly evaluate the physiological behavior of the individual patient [1-3, 5]. In addition, because most of the control algorithms are not amenable to multiple inputs and multiple outputs, the measured blood glucose level is generally, the only input implemented, and insulin delivery is the only implemented output.
The PID control algorithm produces an insulin profile similar to the secretion profile done by the beta cells extrapolated by three components [1]. Some controllers include a subset of components, for example, a proportional—derivative (PD) controller includes the proportional and derivative components to improve robustness. Both PID and PD use the measured blood glucose (BG) level as the only input and ignore other parameters, such as previous administered insulin doses. The MPC is based on mathematical model and equations which describes the glucose level response to different insulin doses and carbohydrate consumption. As the response to different insulin treatment is implied by the set of equations, an optimal treatment may be found and applied accordingly. The mathematical model is subject specific, and depends upon system identification phase to estimate the required parameters [3]. The main drawback of MPC in relation to glucose control is the need of a good crisp mathematical model and a good method to estimate its parameters in order to describe the physiological behavior of the patient. However, due to the complexity of biological systems, these models are subject to extreme uncertainties, which make it very hard to evaluate and define the model properly. Most of the attempts in the past to develop Subcutaneous (S.C.) closed loop system used linear control methodology to control the non-linear biological system [2, 5] and disregarded the uncertainty of the biological system and the measuring devices. In addition, it is quite difficult to implement multiple inputs and multiple outputs using these methods.
General Description
There is a need in the art for a novel approach in management of the insulin delivery to patients. Such need is associated with the following.
Conventional insulin pumps initially require a physician to arrive to the required global pump settings and/or “request” a response from the patient to perform a test for the appropriateness of insulin pump settings (previously set by a physician). This, however, requires higher degree of expertise from the physician and also is based on an assumption that the patient responds correctly to the requests. Such global pump settings remain constant during operation of the insulin pump until such time that the physician or treated patient manually resets them. Insulin pump settings generated based on such conventional approach would thus unavoidably be too sensitive to the cooperation with the patient.
The present invention solves the above problems by providing according to one broad aspect a novel technique for accurate and reliable tailor made insulin pump settings derived from raw log data accumulating for example in conventional blood glucose monitoring device(s). The present invention, therefore provides unsupervised determination of global insulin pump settings, e.g. even without human interpretation or assumptions as to the nature in which data was obtained. The technique of the present invention of such unsupervised determination of insulin pump settings from received data is actually absolutely independent from the need of cooperative participation on the part of the diabetic patient.
In contrast to standardized procedure for testing, which require active participation or cooperation of the part of the diabetic patient and/or a physician for arriving to accurate and accountable pump settings, the monitoring technique of the present invention conducts a retrospective analysis of the log/raw data, isolates informative data from raw residual data, and applies unsupervised learning procedures to arrive to the optimal global insulin pump settings. The technique of the present invention thus provides the capability to extract informative data from the raw data, which according to the known techniques is ignored or is exclusively subject to human expert analysis. It should be understood that retrospective analysis utilized in the invention is aimed at calculating global insulin pump settings extracted from historical measured data collected during a certain time interval of several days (at least two days) which forms the raw log data input to the unsupervised data processor. The minimal time interval for the purposes of the invention, i.e. for retrospective analysis, is actually defined by the collection of various types of information (as will be described further below) and the ability of the system (data processor) of identifying different information pieces. The inventors have found that, practically, a two-day data record is sufficient for the calculation of the pump settings. By settings the lower bound of 2 days for the time window for the unsupervised retrospective analysis, the present invention utilizes accumulation of substantial raw log data of the treated patient, however, accumulation of more information is preferred to permit analysis of plethora of data sections of patient information. The historical measured data comprises a plurality of data pieces which according to the invention is appropriately identified, sectioned, isolated and retrospectively analyzed to calculate global insulin pump settings from the historical performance in these data sections. It should also be understood that the invention provides for dealing with the raw data while enabling calculate global insulin pump settings, namely pump settings which are optimal and which should be maintained.
According to some embodiments of the present invention, the above monitoring system further includes a processing unit having additional components/modules (software and/or hardware) for additional processing of other relevant data. The processing unit receives the output of the unsupervised controller, and input parameters corresponding to the measured data, the first processed data and a reference data including said individualized patient's profile related data, individualized patient's treatment history related data. The processing unit is configured and operable for generating a treatment recommendation accordingly. The treatment recommendation may be either sent automatically to the insulin pump or may be presented to an authorized person (e.g. a physician or the patient) through a user interface for choosing whether to apply the treatment recommendation or not.
According to a broad aspect of the present invention, there is provided a monitoring system for use with diabetic treatment management, the monitoring system comprising:                a communication interface configured and operable to permit access to stored raw log data obtained over a certain time and being time spaced data points of glucose measurements, meals consumed and insulin delivery;        a control unit comprising an unsupervised learning controller configured and operable to receive and process said raw log data, to determine an informative data piece from residual log data portion of said raw log data and select said informative data piece for retrospective analysis to calculate individualized patient's profile related data comprising at least one of global insulin pump settings of basal rate (or basal plan), correction factor (CF), carbohydrate ratio (CR) and insulin activity curve (AIF).        
In some embodiments, the raw log data is acquired in accordance with a preprogrammed sampling pattern. The unsupervised learning controller is configured and operable determine each of said parameters from a part of said informative data piece corresponding to a selected time slot of said certain time. Therefore, said informative data piece relating insulin pump settings are identified in the corresponding time slots.
The unsupervised learning controller is configured and operable for analyzing said informative data piece and selects the appropriate time slot for calculation of each of said parameters; the global insulin pump parameters being of basal rate (or basal plan), correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters.
In some embodiments, the received raw log data corresponds to a memory image at the access time irrespective of any user interaction.
In another aspect, the present invention relates to a monitoring system for use with diabetic treatment management, the monitoring system comprising:                a communication interface configured and operable to permit access to stored data being time spaced data points of glucose measurements, meals consumed and insulin delivery;        a control unit comprising a data processor utility for providing retrospective analysis of said data and determining at least one global insulin pump setting of basal rate (or basal plan), correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, wherein said processor utility is operable to determine each of said parameters by processing a data piece of said received data corresponding to a selected time slot of said certain period of time.        
In some embodiments, the processor utility is configured and operable for analyzing the received data and selects the time slot in said certain period of time for determination of each of said parameters.
In some embodiments, the control unit comprises a controller associated with said communication interface and preprogrammed for receiving said data according to a predetermined sampling time pattern.
The received stored data can be that of a memory image at the access time irrespective of any user interaction.
The system can comprise a memory module configured and operable to maintain the stored data.
The analyzing can include sectioning the stored data; thereby to obtain stored data within a predetermined time window. Where the predetermined time window is a Basal data Section (BaS) the calculated insulin pump settings being selected is basal rate or basal plan. Where said predetermined time window is a Meals data. Section (MS) the calculated insulin pump settings being selected from being Active Insulin Function (AIF), correction factor (CF) or carbohydrate ratio (CR). In case, the predetermined time window is a Bolus data Section (BS) the calculated insulin pump settings being selected from correction factor (CF) or Active Insulin Function (AIF). The stored data can be obtained from a remote controller such as for example from a controller or module of an insulin pump delivery device. In some embodiments, the stored data is accessible via random asynchronous operation which is independent of a user operation. In some embodiments, the stored data is a memory image of a remote controller independently accumulating the raw log data input. The remote controller(s) can independently accumulate said information which records the everyday routine of the treated patient. The information indicative glucose sensor readings, insulin delivery and meals recordation can be a file being obtained from the remote controller independently accumulating said information.
The file can be downloaded from a network and stored in the memory module.
In another aspect, the present invention relates to a method for use in determination of insulin pump settings, the method comprising: performing unsupervised learning of the insulin pump settings, said unsupervised learning comprising:                obtaining raw log data input accumulated on one or more glucose monitoring units recording glucose levels of a single treated patient along a certain time window;        determining informative data piece from raw log data input being sectioned to data sections, the informative data piece being determined from said data section; and        calculating insulin pump settings from the informative data piece, wherein said settings include at least one parameter of basal plan, Carbohydrate Ratio (CR), Correction Factor (CF) or Active Insulin Function (AIF).        
The sectioning procedure of the raw log data provides predetermined data sections which can be any of Basal Section (Bas), Bolus Section (BS), or Meal Section (MS). The method utilizes aligning procedure to provide plurality of data portions of said raw log data input along a shared time axis.
The method can further include determining a representative data point having both a value of aggregated blood glucose levels and a time stamp; the value of aggregated blood glucose level is thus paired to a selected basal period; the representative data point indicates a basal rate determination for the selected basal period.
In some embodiments, the raw log data input of said Basal Section (Bas) includes a series of basal rates as a function of time. The method can thus include:                determining a time delay characterizing the treated patient at said Basal Section (Bas), said time delay being between a basal treatment rate and changes in the glucose level;        obtaining a plurality of selected basal rates at a delivery time, a respective paired glucose level being at the time delay measured from the delivery time; and        determining a resultant basal rate from the plurality of selected basal rates which minimizes a change in the glucose level.        
In some embodiments the method comprises determining an Active Insulin Function (AIF) by carrying out the following method:                obtaining a set of glucose measurements and paired time stamps for the raw log data in the time section;        normalizing each glucose measurement of the set thereby obtaining a series of normalized glucose measurements and paired time stamp; and        processing said normalized glucose measurements and paired time stamp into a substantially monotonic non-increasing series; thereby obtaining the Active Insulin Function (AIF).        
In some embodiments, the method includes determining plurality of glucose level and paired practical carbohydrate ratios for the MS Section; the paired practical carbohydrate ratios being candidate carbohydrate ratios defining a curve. The final carbohydrate ratio (CR) setting is determined from the candidate practical carbohydrate ratios.
In some embodiments, a correction factor (CF) is determined for the meal and is calculated by processing the AIF to estimate the active insulin in the MS Section and a just-in-time carbohydrate ratio (CR).
The correction factor (CF) can be modified in accordance with the following parameters:                a proportion between a minimum sensor reading during a time window or section, a lowest blood glucose reading recorded outside impending hypoglycaemia and hypoglycaemia time periods; and        a maximum sensor reading in a time slot prior to obtaining the minimum sensor reading.        
In some embodiments, a plurality of candidate correction factors (CF) are determined and the correction factor (CF) setting is determined by a voting procedure performed with those candidate correction factors (CF).
In another aspect, the present invention provides a method for determining an Active Insulin Function (AIF) for use in insulin treatment of a patient, the method comprising:                obtaining raw log data obtained over a certain time and being indicative of glucose measurements of the patient, the raw log data being sectioned, containing data obtained at a time section;        obtaining a set of glucose measurements and paired time stamps for the raw log data in the time section;        normalizing each glucose measurement of the set thereby obtaining a series of normalized glucose measurements and paired time stamp; and        processing said normalized glucose measurements and paired time stamp into a substantially monotonic non-increasing series; thereby obtaining the Active Insulin Function (AIF).        
In another aspect, the present invention provides, a control unit for use with diabetic treatment management, the control unit comprising: a data processor utility configured and operable as an unsupervised learning controller preprogrammed for processing raw log data input obtained over a certain time and being indicative of glucose measurements, meals events and insulin delivery, the processing comprising determining an informative data piece from residual log data portion of said raw log data and selecting said informative data piece for further processing to determine at least one of basal rate (or basal plan), correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, and generating global insulin pump settings.
According to some embodiments of the present invention, the above-described monitoring system further comprises a processing unit comprising: a first processor module and a second processor module. The first processor module is configured for processing measured data indicative of blood glucose level and generating first processed data indicative thereof. The second processor module comprises at least one fuzzy logic module. The fuzzy logic module receives input parameters corresponding to the measured data, the first processed data and a reference data including said individualized patient's profile related data, individualized patient's treatment history related data, processes the received parameters to produce at least one qualitative output parameter indicative of patient's treatment parameters; such that said second processor module determines whether any of the treatment parameters is to be modified and generate corresponding output data which can be supplied directly to the pump and/or presented through a user interface to an authorized person (patient and/or physician) for a decision making and/or recording.
In a variant, input parameters include at least one of the following input parameters: past blood glucose level trend, current blood glucose level, future blood glucose level trend, future blood glucose level.
The at least one fuzzy logic module may be characterized by at least one of the following: (i) it comprises a set of rules associated with contribution factors and at least one fuzzy engine utilizing one or more member functions modeled for translating the input parameters into at least one qualitative output parameter; and (ii) is configured and operable to provide the at least one output parameter comprising data indicative of at least one of bolus glucagon, bolus insulin and basal insulin treatment, said second processor module thereby providing control to range output treatment suggestion based on the output parameter of the fuzzy logic module.
In another variant, said processing unit comprises a third processor module receiving said at least one qualitative output parameter of the fuzzy logic module and said input parameters corresponding to the measured data, the first processed data and the reference data, and processing said at least one output parameter said input parameters to determine whether any of the treatment parameters is to be modified and generate corresponding output data which can be supplied directly to the pump and/or presented through the user interface to an authorized person (patient and/or physician) for a decision making and/or recording, said treatment parameters comprising at least one of dosing of insulin and glucagon to be delivered.
In a further variant, the at least one output parameter of the at least one fuzzy logic module comprises data indicative of at least one of bolus glucagon, bolus insulin and basal insulin treatment, and said second processor module thereby provides control to range output treatment suggestion based on the output parameter of the fuzzy logic module, the third processor receiving the control to range output treatment suggestion, and determining said amount in accordance with at least one of a glucose target of the patient's profile, patient's insulin or glucagon pharmacodynamics, and said measured data.
In a further variant, the processing unit is operable to update and/or calibrate said individualized patient's profile related data during treatment or during monitoring procedure.
Optionally, said individualized patient's profile related data comprises parameters selected from at least one of global pump settings, insulin sensitivity, glucagon sensitivity, basal plan, insulin/glucagon pharmacokinetics associated data, glucose target level or target range level, and insulin/glucagon activity model.
Said individualized patient's treatment history related data may comprise patient's insulin delivery regimen given to the patient at different hours of the day.
According to some embodiments of the present invention, said second processor module comprises a fuzzy logic module operable in response to an event being invoked by a detector module analyzing at least one pattern of glucose levels indicative of at least one event, said event comprising at least one of sleep, meal, exercise and disease event or rest.
Said system may be configured and operable to alternate between at least two fuzzy logic modules, each handling a different event.
In a variant, said second processor module is operable as a meal treatment module and is configured to monitor the blood glucose level.
In another variant, the input parameters further include at least one of the following input parameters: time elapsed between detected special events, blood glucose level with respect to said special event.
Optionally, said measured data is obtained at a certain time, said measured data comprising at least one of current and past glucose levels relative to said certain time.